import numpy as  np

def sigmoid(x):
    # our activation fucntion: f(x) = 1/(1+e^(-x))
    # np.exp(-x): 它计算了自然数 e 的 −x 次幂
    return 1 / (1 + np.exp(-x))

def deriv_sigmoid(x):
    # 求导函数
    # derivateive of sigmoid: f'(x) = f(x) *(1 - f(x))
    fx = sigmoid(x)
    return fx * (1 - fx)

def mse_loss(y_true,y_pred):
    # y_true and y_pred are numpy arrays of the same length
    return ((y_true - y_pred) **2 ).mean()

class NeuronNetWork:
    def __init__(self):
        # weights
        self.w1 = np.random.normal()
        self.w2 = np.random.normal()
        self.w3 = np.random.normal()
        self.w4 = np.random.normal()
        self.w5 = np.random.normal()
        self.w6 = np.random.normal()

        # Biases
        self.b1 = np.random.normal()
        self.b2 = np.random.normal()
        self.b3 = np.random.normal()

    def feedforward(self,x):
        # x is a numpy array with 2 elements
        h1 = sigmoid(self.w1 * x[0] + self.w2 * x[1] + self.b1)
        h2 = sigmoid(self.w3 * x[0] + self.w4 * x[1] + self.b2)
        o1 = sigmoid(self.w5 * h1 + self.w6 * h2 + self.b3)
        return o1

    def train(self,data,all_y_trues):
        learn_rate = 0.1
        epochs = 1000

        for epoch in range(epochs):
            for x , y_true in zip(data,all_y_trues):
                sum_h1 = self.w1 * x[0] + self.w2 * x[1] + self.b1
                h1 = sigmoid(sum_h1)

                sum_h2 = self.w3 * x[0] + self.w4 * x[1] + self.b2
                h2 = sigmoid(sum_h2)

                sum_o1 = self.w5 * h1 + self.w6 * h2 + self.b3
                o1 = sigmoid(sum_o1)
                y_pred = o1

                # --- calculate partial derivatives
                # --- Naming: d_L_d_w1 represents "partial L / partial w1"
                d_L_d_ypred = -2 * (y_true - y_pred)

                # Neuron o1
                d_ypred_d_w5 = h1 * deriv_sigmoid(sum_o1)
                d_ypred_d_w6 = h2 * deriv_sigmoid(sum_o1)
                d_ypred_d_b3 = deriv_sigmoid(sum_o1)

                d_ypred_d_h1 = self.w5 * deriv_sigmoid(sum_o1)
                d_ypred_d_h2 = self.w6 * deriv_sigmoid(sum_o1)

                # Neuron h1
                d_h1_d_w1 = x[0] * deriv_sigmoid(sum_h1)
                d_h1_d_w2 = x[1] * deriv_sigmoid(sum_h1)
                d_h1_d_b1 = deriv_sigmoid(sum_h1)

                # Neuron h2
                d_h2_d_w3 = x[0] * deriv_sigmoid(sum_h2)
                d_h2_d_w4 = x[1] * deriv_sigmoid(sum_h2)
                d_h2_d_b2 = deriv_sigmoid(sum_h2)

                # --- update weights and biases
                # Neuron h1
                self.w1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w1
                self.w2 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w2
                self.b1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_b1

                # Neuron h2
                self.w3 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w3
                self.w4 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w4
                self.b2 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_b2

                # Neuron o1
                self.w5 -= learn_rate * d_L_d_ypred * d_ypred_d_w5
                self.w6 -= learn_rate * d_L_d_ypred * d_ypred_d_w6
                self.b3 -= learn_rate * d_L_d_ypred * d_ypred_d_b3

                # --- calculate total loss at the end of each epoch
            if epoch % 10 == 0:
                y_preds = np.apply_along_axis(self.feedforward,1,data)
                loss = mse_loss(all_y_trues,y_preds)
                print("Epoch %d loss: %.3f" % (epoch,loss))

data = np.array([
    [-2,-1], # Alice
    [25,6], # Bob
    [17,4], # Charlie
    [-15,-6] # Diana
])

all_y_trues = np.array([
    1,# Alice
    0, # Bob
    0, # Charlie
    1 # Diana
])

# Train neural network
network = NeuronNetWork()
network.train(data,all_y_trues)

emily = np.array([-7,-3])  ## 128 pinds, 63 inches
frank = np.array([20,2])  ## 155 pinds, 68 inches
bob = np.array([-3,-4])  ## 132 pinds, 64 inches

print("emily : %.3f" % network.feedforward(emily))   # 0.967 - F
print("frank : %.3f" % network.feedforward(frank))   # 0.037 - M
print("bob : %.3f" % network.feedforward(bob))       # 0.955 - F








